Explainable AI based Interventions for Pre-season Decision Making in Fashion Retail

Sajja, Shravan; Aggarwal, Nupur; Mukherjee, Sumanta; Manglik, Kushagra; Dwivedi, Satyam; Raykar, Vikas C.

doi:10.1145/3430984.3430995

Cited by 12 publications

(5 citation statements)

References 11 publications

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“…While there have not been studies on this use case for policy administration decisions to the best of our knowledge, there have been a few efforts in other domains where researchers have investigated using explainable ML for supporting recommending actions. For instance, Afzaal et al (2021) showed that explanations of student performance predictions can be used to recommend actions to students in self-regulated learning, Albreiki (2022) studied how explanations from ML can be used to recommend remedial actions to low-performing students with the goal of improving learning outcomes, and Sajja et al (2021) demonstrated the use of explainable ML predictions of consumer behavior in helping fashion designers plan for new products.…”

Section: The Role Of Explainable ML In Public Policy Applicationsmentioning

confidence: 99%

Explainable machine learning for public policy: Use cases, gaps, and research directions

et al. 2023

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Explainability is highly desired in machine learning (ML) systems supporting high-stakes policy decisions in areas such as health, criminal justice, education, and employment. While the field of explainable ML has expanded in recent years, much of this work has not taken real-world needs into account. A majority of proposed methods are designed with generic explainability goals without well-defined use cases or intended end users and evaluated on simplified tasks, benchmark problems/datasets, or with proxy users (e.g., Amazon Mechanical Turk). We argue that these simplified evaluation settings do not capture the nuances and complexities of real-world applications. As a result, the applicability and effectiveness of this large body of theoretical and methodological work in real-world applications are unclear. In this work, we take steps toward addressing this gap for the domain of public policy. First, we identify the primary use cases of explainable ML within public policy problems. For each use case, we define the end users of explanations and the specific goals the explanations have to fulfill. Finally, we map existing work in explainable ML to these use cases, identify gaps in established capabilities, and propose research directions to fill those gaps to have a practical societal impact through ML. The contribution is (a) a methodology for explainable ML researchers to identify use cases and develop methods targeted at them and (b) using that methodology for the domain of public policy and giving an example for the researchers on developing explainable ML methods that result in real-world impact.

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Section: The Role Of Explainable ML In Public Policy Applicationsmentioning

confidence: 99%

Explainable machine learning for public policy: Use cases, gaps, and research directions

et al. 2023

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“…Pre-season forecasting in fashion retail was also discussed in [5]. The authors point out, that the typical time-series methods can be introduced for this problem, however in most cases the retailers need to forecast new products, so in historical data there is no time-series linked precisely to the forecast product.…”

Section: Related Workmentioning

confidence: 99%

Demand forecasting in the fashion business — an example of customized nearest neighbour and linear mixed model approaches

Henzel¹,

Wawrowski²,

Kubina³

et al. 2022

Annals of Computer Science and Information Systems

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The fashion industry is characterised by the need to make demand forecasts in advance and for highly volatile products for which we often have no sales history at the time the forecasts are made. For this reason, it is necessary to propose forecast mechanisms that can cope with the given conditions. Such forecasts can be based on expert predictions for generalized product categories. In this case, the task of machine learning forecasting methods would be to divide the aggregate prediction into forecasts for individual products, in each colour and size.In the paper, we present several approaches to this specific task. We present the use of the naive method, custom nearest neighbour approach, parametric linear mixed model and an ensemble approach. Overall, the best results we obtained for the ensemble method. Our research was based on real data from fashion retail.

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“…AI application is found in the earliest phases of PL, namely product design. Explainable AI helps in understanding consumer shopping behaviour and using it to further optimize product design, development, and sourcing (Sajja et al, 2020). ANNs are used for assessing environmental impact of the products in the design and conception phase (Park & Seo, 2003).…”

Section: Product Lifecyclementioning

confidence: 99%

AI-enabled Enterprise Information Systems for Manufacturing

Zdravković

Panetto

Weichhart

2021

Enterprise Information Systems

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Research work presented in this paper considers the contemporary Enterprise Information Systems (EIS) functional architecture and carries out the literature review of AI applications integrated in Customer Relationship Management, Supply Chain Management, Inventory and logistics, Production Planning and Scheduling, Finance and accounting, Product Lifecycle Management and Human Resources, with special attention to the manufacturing enterprises. As a review outcome, the enhanced capabilities are identified in each of those processes and proposed as AI services. AI-enablement is related to the capabilities of the enterprise to implement improved decision making or automation by using (a) Machine Learning (ML) models or (b) logicbased systems. It is a process of the enterprise transformation leading to the convergence of the 4 major disruptive technologies, namely Industrial Internet of Things (or Cyber-Physical Systems), Agent-based Distributed Systems, Cloud Computing and Artificial Intelligence (non-symbolic AI like Machine Learning, Neural Networks; symbolic AI like Reasoning and Logic/Model-based AI).

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Explainable AI based Interventions for Pre-season Decision Making in Fashion Retail

Cited by 12 publications

References 11 publications

Explainable machine learning for public policy: Use cases, gaps, and research directions

Explainable machine learning for public policy: Use cases, gaps, and research directions

Demand forecasting in the fashion business — an example of customized nearest neighbour and linear mixed model approaches

AI-enabled Enterprise Information Systems for Manufacturing

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